When using GAPGOM, please cite the following;


GAPGOM (novel Gene Annotation Prediction and other GO Metrics) is an R package with tools and algorithms for estimating correlation of gene expression, enriched terms in gene sets, and semantic distance between sets of gene ontology (GO) terms. This package has been made for predicting the annotation of un-annotated gene(s), in particular with respect to GO, and testing such predictions. The prediction is done by comparing expression patterns between a query gene and a library of annotated genes, and annotate the query gene by enriched terms from the set of genes with similar expression pattern (often described as “guilt by association”).

For correlation of gene expression, GAPGOM is introducing LNCRNA2GOA which is a novel tool. The main interface for expression data is currently the Fantom5 data, using Bioconductor’s ExpressionSet class.

For semantic similarity of GO terms (in particular for testing predictions), the package is using TopoICSim. It makes use of GO data via the GOSemSim package with the godata() interface.

GO consists of three main Ontologies; molecular function (MF), biological process (BP) and cellular component (CC).


Before installing, the package has quite a few dependencies in both cran and Bioconductor. You can run the following block of code (preferably line-by-line because of prompts) to install these and the package itself.

Expression data interfaces

ID support

As of v0.2.7 and up, all AnnotationDbi IDs should be supported. However, we recommend usage of EntrezIDs because this is the most widely supported ID in this and other packages. If you find issues with respect to ID support, please notify this issue on the package repository. If you want to (or have to) convert IDs manually, the BiomaRt package is recommended. However, translating IDs to other types is lossy and does not always translate well.

Expression data (FANTOM5)

As of right now, the package has one main dataset interface for expression data; The Fantom5 dataset. For other datasets, an ExpressionSet has to be manually made as described later in this chapter. There are a few helper functions to make this data usable. The Fantom5 dataset is only available for the human and mouse genome. Examples of helper functions/interfaces can be found below;

Do note that all standard columns are necessary when converting to an ExpressionSet in this way!

Manually specifying an ExpressionSet

Because loading expression data right now is a bit limited, this paragraph will describe how to convert expression data to an ExpressionSet object. We will give an example with randomly selected expression values and IDs. In some cases if you want something specific, defining it this way can actually be better (More control over extra data that goes into the object/better interoperability between packages).

Minimal requirement for an ExpressionSet;

  • expression values
  • Unique IDs of a certain type. AnnotationDbi keys are the only IDs that are supported right now.

Each row of expression values should have corresponding IDs, with the ID-type as its column name.

Random expression value generation;

# select x random IDs
x_entries <- 1000

go_data <- GAPGOM::set_go_data("human", "BP", computeIC = FALSE)
#> Loading required package:
#> Loading required package: AnnotationDbi
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: parallel
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:parallel':
#>     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
#>     clusterExport, clusterMap, parApply, parCapply, parLapply,
#>     parLapplyLB, parRapply, parSapply, parSapplyLB
#> The following objects are masked from 'package:stats':
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, append,
#>, basename, cbind, colnames, dirname,,
#>     duplicated, eval, evalq, get, grep, grepl, intersect,
#>     is.unsorted, lapply, mapply, match, mget, order, paste, pmax,
#>, pmin,, rank, rbind, rownames, sapply,
#>     setdiff, sort, table, tapply, union, unique, unsplit, which,
#>     which.max, which.min
#> Loading required package: Biobase
#> Welcome to Bioconductor
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> Loading required package: IRanges
#> Loading required package: S4Vectors
#> Attaching package: 'S4Vectors'
#> The following object is masked from 'package:base':
#>     expand.grid
#> preparing gene to GO mapping data...
random_ids <- unique(sample(go_data@geneAnno$ENTREZID, x_entries)) # and only keep 
# uniques

# make general dataframe. 
expressions <- data.frame(random_ids)
colnames(expressions) <- "ENTREZID"

# n expression values depending on the amount of unique IDs that are present
expressionvalues <- abs(rnorm(length(random_ids)*6))*x_entries
expressions[,2:7] <- expressionvalues
#>   ENTREZID        V2         V3        V4        V5        V6        V7
#> 1     2047  977.9075  781.63778 1019.6952 2641.1646 1506.5371  635.7274
#> 2    65125 1365.2751   65.30562  336.7877 1849.5753  922.1089  592.2731
#> 3     6531 1567.2573 1051.28188   43.9102  634.3948  372.7208  363.4411
#> 4     6657  643.4315 1257.73833  456.5400  842.1561  349.1980 1000.6480
#> 5      358 1044.5030 2607.72778  135.9698  867.7302   18.4410 1179.3717
#> 6     8829  394.1429  961.68728  237.4838 1235.0147 1674.7981  697.2044

Converting the expression dataframe to an ExpressionSet;

LNCRNA2GOA (expression similarity)


The LNCRNA2GOA (long non-coding RNA to GO Annotation) or expression_prediction() uses various methods/measures to determine similar genes with similar expression pattern; pearson, spearman, kendall, sobolev and fisher. This calculates scores between expression value sets given a query gene. These scores are used to identify genes for the enrichment analysis, which will be sorted by significance before being returned. It is also possible to find similar expression patterns by using the combined method. The Sobolev and Fisher metrics are so far unique to this package (at least in the context of this type of analysis), all the others are standard methods from the R cor() function. Details of the novel methods are described below (quoted from paper [1], references edited).

Sobolev metric

In this section, we use definitions and notations as in [2]. We start with the usual p-inner product. Let \(f\), \(g\) be real-valued functions (in this case \(f\) and \(g\) values are the expression vectors of two genes \(f\) and \(g\)):

\(\langle f,g\rangle_{p} = (\sum_{k=1}^{n}\mid f_k.g_k\mid^p)^\frac{1}{p}\)


By this notation, Sobolev inner product, norm and meter of degree \(k\) respectively can be defined by:

\(\langle f,g\rangle_{p,a}^{S} = \langle f,g\rangle_p+\alpha\langle D^kf,D^kg\rangle_p\)


\(\mid\mid f\mid\mid_{p,k,\alpha}^S = \sqrt{\langle f,f\rangle_{p,\alpha}^S}\)


\(d_{p,k,\alpha}^S(f,g) = \mid\mid f-g\mid\mid_{p,k,\alpha}^S\)


where \(D^k\) is the \(k\)th differential operator. For the special case \(p=2\) and \(\alpha=1\) an interesting connection to the Fourier-transform of analysis can be made; let \(\hat{f}\) be the Fourier-transform \(f\)

\(\hat{f}(\omega_k) = \sum_{j=1}^{N-1}g_jexp(-i\frac{2\pi kj}{N})\)


Where \(\omega_k=\frac{2\pi k}{N}\) and \(i=\sqrt{-1}\). Finally the norm can be written as

\(\mid\mid f\mid\mid_{2,k,1}^S = \sqrt{\sum_{j=1}^{N-1}(1+\omega_j)^k\mid \hat{f}(\omega_j)\mid^2}\)


In this work metric (5) with norm (7) and \(k=1\) was used.

Fisher metric

In this section we use definitions and notations such as in [3]. To define Fisher information metric we first introduce the n-simplex \(P_n\) defined by

\(P_n=\{x\in R^{n+1}:\forall i, x_n \ge0,\sum_{i=1}^{n+1}xi=1\}\)


The coordinates \(\{x_i\}\) describe the probability of observing different outcomes in a single experiment (or expression value of a gene in \(i\)th cell type). The Fisher information metric on \(P_n\) can be defined by

\(Jij = \sum_{k=1}^{n+1}\frac{1}{x_k}\frac{\partial x_k}{\partial x_i}\frac{\partial x_k}{\partial x_j}\)


We now define a well-known representation of the Fisher information as a pull-back metric from the positive n-sphere \(S_n^+\)

\(S_n^+=\{x\in R^n;\forall i,x_n\ge0,\sum_{i=1}^{n+1}x^2=1\}\)


The transformation \(T: P_n\to S_n^+\) defined by

\(T(x)=(\sqrt{x_1}, \dots, \sqrt{x_n+1})\)


pulls back the Euclidean metric on the surface of the sphere to the Fisher information on the multinomial simplex. Actually, the geodesic distance for \(x,y \in P_n\) under the Fisher information metric may be defined by measuring the length of the great circle on \(S_n^+\) between \(T(x)\) and \(T(y)\)

\(d(x,y) = acos(\sum_{i=1}^{n+1}\sqrt{x_iy_i})\)


The LNCRNA2GOA method can also be used on other novel genes besides lncRNAs.


Scores + Enrichment

The following example is an arbitrary use-case. Meaning that this is just an example and does not (necessarily) imply a certain question/real life use-case. id_select_vector represents a vector of gene ids that you’d want to use for annotation enrichment (if left empty, algorithm will use all available gene ids in the ExpressionSet).

GOID Ontology Pvalue FDR Term used_method
GO:0006810 BP 0.0001042 0.0003769 transport fisher
GO:0007165 BP 0.0010725 0.0070414 signal transduction kendall
GO:0006355 BP 0.0031231 0.0095531 regulation of transcription, DNA-templated pearson
GO:0045893 BP 0.0027479 0.0097134 positive regulation of transcription, DNA-templated fisher
GO:0006366 BP 0.0041102 0.0122466 transcription by RNA polymerase II sobolev
GO:0006468 BP 0.0091572 0.0443554 protein phosphorylation spearman
GO:0006351 BP 0.0158478 0.0475434 transcription, DNA-templated pearson

Here we display the results, you can see that it has 6 columns;

  • GOID

Describing the significantly similar GO terms

  • Ontology Describing the ontology of the result.
  • Pvalue The P-value/significance of the result.
  • FDR bonferoni normalized P-value
  • Term The description of the GO term.
  • used_method Used scoring method for getting the result.

Besides this, there is an optional parameter for different GO labeling/annotation; id_translation_df. This dataframe should contain the following;

  • rownames \(\to\) rownames of the expression set
  • first column \(\to\) Gene ID (e.g. EntrezID). Gene should be the same as in the expression dataset.
  • second colum \(\to\) GO IDs.

This can also drastically improve calculation time as most of the time is spent on querying for this translation.

Only scores

There is also another algorithm that allows you to just calculate scores and skip the enrichment;

original_ids score used_method
ENSG00000224505 ENSG00000224505 0.2004647 pearson
ENSG00000139144 ENSG00000139144 0.1011604 pearson
ENSG00000265787 ENSG00000265787 0.1247753 pearson
ENSG00000204539 ENSG00000204539 0.1396684 pearson
ENSG00000253563 ENSG00000253563 0.0892708 pearson
ENSG00000188784 ENSG00000188784 0.0863590 pearson
ENSG00000042304 ENSG00000042304 0.0726566 pearson
ENSG00000248787 ENSG00000248787 0.2197304 pearson
ENSG00000269305 ENSG00000269305 0.0561735 pearson
ENSG00000241933 ENSG00000241933 0.0606906 pearson
ENSG00000132640 ENSG00000132640 0.1382561 pearson
ENSG00000137634 ENSG00000137634 0.0981431 pearson
ENSG00000186994 ENSG00000186994 0.1811896 pearson
ENSG00000095906 ENSG00000095906 0.5401186 pearson
ENSG00000187871 ENSG00000187871 0.0167506 pearson
ENSG00000254350 ENSG00000254350 0.0477682 pearson
ENSG00000151247 ENSG00000151247 0.0106081 pearson
ENSG00000109061 ENSG00000109061 0.0682357 pearson
ENSG00000257594 ENSG00000257594 0.0225258 pearson
ENSG00000253720 ENSG00000253720 0.1931214 pearson
ENSG00000180638 ENSG00000180638 0.0566312 pearson
ENSG00000234279 ENSG00000234279 0.1001705 pearson
ENSG00000188032 ENSG00000188032 0.1129793 pearson
ENSG00000171161 ENSG00000171161 0.0709871 pearson
ENSG00000250411 ENSG00000250411 0.0589016 pearson
ENSG00000260244 ENSG00000260244 0.2590438 pearson
ENSG00000063601 ENSG00000063601 0.0637382 pearson
ENSG00000249605 ENSG00000249605 0.0834436 pearson
ENSG00000182368 ENSG00000182368 0.0104575 pearson
ENSG00000224819 ENSG00000224819 0.1137457 pearson
ENSG00000196531 ENSG00000196531 0.0298562 pearson
ENSG00000085377 ENSG00000085377 0.0761354 pearson
ENSG00000129195 ENSG00000129195 0.3678937 pearson
ENSG00000227608 ENSG00000227608 0.1634545 pearson
ENSG00000250043 ENSG00000250043 0.0943256 pearson
ENSG00000269843 ENSG00000269843 0.3473258 pearson
ENSG00000181690 ENSG00000181690 0.4336276 pearson
ENSG00000125246 ENSG00000125246 0.3062198 pearson
ENSG00000262861 ENSG00000262861 0.1053504 pearson
ENSG00000173171 ENSG00000173171 0.1348866 pearson
ENSG00000248455 ENSG00000248455 0.0930772 pearson
ENSG00000265114 ENSG00000265114 0.1114366 pearson
ENSG00000166173 ENSG00000166173 0.2045608 pearson
ENSG00000068489 ENSG00000068489 0.1446615 pearson
ENSG00000183474 ENSG00000183474 0.0317519 pearson
ENSG00000138326 ENSG00000138326 0.1441870 pearson
ENSG00000101096 ENSG00000101096 0.1657225 pearson
ENSG00000235151 ENSG00000235151 0.2721271 pearson
ENSG00000154265 ENSG00000154265 0.1235900 pearson
ENSG00000177946 ENSG00000177946 0.5389089 pearson
ENSG00000225302 ENSG00000225302 0.0224971 pearson
ENSG00000237560 ENSG00000237560 0.1250799 pearson
ENSG00000261195 ENSG00000261195 0.1342620 pearson
ENSG00000235123 ENSG00000235123 0.0486064 pearson
ENSG00000260092 ENSG00000260092 0.1116397 pearson
ENSG00000254431 ENSG00000254431 0.1893861 pearson
ENSG00000118526 ENSG00000118526 0.1717508 pearson
ENSG00000261049 ENSG00000261049 0.0393922 pearson
ENSG00000254489 ENSG00000254489 0.1097308 pearson
ENSG00000176697 ENSG00000176697 0.3079547 pearson
ENSG00000250519 ENSG00000250519 0.0213221 pearson
ENSG00000263821 ENSG00000263821 0.0863590 pearson
ENSG00000065665 ENSG00000065665 0.0021354 pearson
ENSG00000088836 ENSG00000088836 0.0343418 pearson
ENSG00000254514 ENSG00000254514 0.1114366 pearson
ENSG00000149308 ENSG00000149308 0.0458501 pearson
ENSG00000109534 ENSG00000109534 0.0521644 pearson
ENSG00000204187 ENSG00000204187 0.2636256 pearson
ENSG00000102468 ENSG00000102468 0.0711898 pearson
ENSG00000101457 ENSG00000101457 0.1492439 pearson
ENSG00000204837 ENSG00000204837 0.1985665 pearson
ENSGR0000236871 ENSGR0000236871 0.1737215 pearson
ENSG00000113645 ENSG00000113645 0.1680317 pearson
ENSG00000261617 ENSG00000261617 0.0935473 pearson
ENSG00000177947 ENSG00000177947 0.1092690 pearson
ENSG00000233605 ENSG00000233605 0.1114366 pearson
ENSG00000187486 ENSG00000187486 0.0193328 pearson
ENSG00000159593 ENSG00000159593 0.1764208 pearson
ENSG00000230967 ENSG00000230967 0.0863590 pearson
ENSG00000064961 ENSG00000064961 0.3389281 pearson
ENSG00000257922 ENSG00000257922 0.0255902 pearson
ENSG00000241685 ENSG00000241685 0.4240072 pearson
ENSG00000171747 ENSG00000171747 0.0964942 pearson
ENSG00000152749 ENSG00000152749 0.0533820 pearson
ENSG00000183597 ENSG00000183597 0.5322610 pearson
ENSG00000214955 ENSG00000214955 0.1746615 pearson
ENSG00000166710 ENSG00000166710 0.0462278 pearson
ENSG00000149182 ENSG00000149182 0.0432318 pearson
ENSG00000259645 ENSG00000259645 0.8049258 pearson
ENSG00000137561 ENSG00000137561 0.0933173 pearson
ENSG00000175746 ENSG00000175746 0.1013987 pearson
ENSG00000230645 ENSG00000230645 0.0219265 pearson
ENSG00000254438 ENSG00000254438 0.1059758 pearson
ENSG00000233423 ENSG00000233423 0.1607758 pearson
ENSG00000254726 ENSG00000254726 0.0301782 pearson
ENSG00000006128 ENSG00000006128 0.1607405 pearson
ENSG00000259862 ENSG00000259862 0.1016300 pearson
ENSG00000204054 ENSG00000204054 0.0103759 pearson
ENSG00000185608 ENSG00000185608 0.2245409 pearson
ENSG00000163013 ENSG00000163013 0.0495888 pearson

We can see that this function returns a different dataframe;

  • original_ids The identifier of the gene expression row
  • score The similarity score/correlation calculated by one of the methods.
  • used_method The used method used to calculate the score.

The rownames also represent the gene expression row. Only the first 100 rows are shown, otherwise the table would be quite big. Enrichment and GO annotation/translation needs to be done manually after this step. However, this should be quite doable with a bit of help from GOSemSim.

Original dataset

The original publication used the lncRNA2Function data [4], to test if the results are the same, a small script has been made to reproduce the same results and is located at the package install directory under scripts. The script folder also contains the two original scripts for the algorithm, but not neccesarily its data. The data (along with the scripts) can be found on the following websites:

Besides this, the scripts folder also contains a proof-of-concept script for potentially doing the analysis on unannotated transcripts (by finding the closest gene). This is eventually meant as a sort of substitute to the famous GREAT tool.



TopoICSim or Topological Information Content Similarity, is a method to measure the similarity between two GO terms given the information content and topology of the GO DAG tree. Unlike other similar measures, it considers both the shortest and longest DAG paths between two terms, not just the longest or shortest path(s). The paths along the GO DAG tree get weighted with the information content between two terms.

For the information content the following forumla is used;

\(IC(t) = -log(p(t))\)


Where \(t\) is the (GO) term. The IC is calculated by GOSemSim, and based on the frequency of the specific go term (\(p(t)\)).

A GO tree can be described as a triplet \(\Lambda=(G,\Sigma,R)\), where \(G\) is the set of GO terms, \(\Sigma\) is the set of hierarchical relations between GO terms (mostly defined as is_a or part_of) [5], and \(R\) is a triplet \((t_i, t_j, \xi)\), where \(t_i,t_j\in G\) and \(\xi\in R\) and \(t_i\xi t_j\). The \(\xi\) relationship is an oriented child-parent relation. Top level node of the GO rDAG is the Root, which is a direct parent of the MF, BP and CC nodes. These nodes are called aspect-specific roots and we refer to them as root in the following. A path \(P\) of length \(n\) between two terms \(t_i,t_j\) can be defined as in (23).

\(P:G\times G\to G\times G\dots\times G=G^{n+1};\\P(t_i,t_j) = (t_i,t_j+1,\dots,t_j)\)


Here \(\forall\) \(s\), \(i\le s<j\), \(\exists\xi_s\in \Sigma\), \(\exists\tau_s\in R\), \(r_s=(t_s,t_{s+1}, \xi_s)\). Because \(G\) is an rDAG, there might be multiple paths between two terms, so we represent all paths between two terms \(t_i,t_j\) according to (24).



We use Inverse Information Content (IIC) values to define shortest and longest paths for two given terms \(t_i,t_j\) as shown in (25-27).

\(SP(t_i,t_j) = \underset{P\in A(t_i,t_j)}{argminIIC(P)}\)


\(LP(t_i,t_j) = \underset{P\in A(t_i,t_j)}{argmaxIIC(P)}\)


\(IIC(P) = \sum_{t\in P}\frac{1}{IC(t)}\)


The standard definition was used to calculate \(IC(t)\) as shown in (28)

\(IC(t) = -log\frac{G_t}{G_\mathrm{Tot}}\)


Here \(G_t\) is the number of genes annotated by the term \(t\) and \(G_\mathrm{Tot}\) is the total number of genes. The distribution of IC is not uniform in the rDAG, so it is possible to have two paths with different lengths but with same IICs. To overcome this problem we weight paths by their lengths, so the definitions in (25) and (26) can be updated according to (29) and (30).

\(wSP(t_i,t_j)=SP(t_i,t_j)\times len(P)\)


\(wLP(t_i,t_j)=LP(t_i,t_j)\times len(P)\)


Now let \(ComAnc(t_i,t_j)\) be the set of all common ancestors for two given terms \((t_i,t_j)\). First we define the disjuntive common ancestors as a subset of \(ComAnc(t_i,t_j)\) as in (31).

\(DisComAnc(t_i,t_j) = \{x\in ComAnc(t_i,t_j)\mid P(x, root)\cap C(x)=\varnothing\}\)


Here \(P(x,root)\) is the path between \(x\) and \(root\) and \(C(x)\) is set of all immediate children for \(x\). For each disjuntive common ancestor \(x\) in \(DisComAnc(t_i,t_j)\), we define the distance between \(t_i,t_j\) as the ratio of the weighted shortest path between \(t_i,t_j\) which passes from \(x\) to the weighted longest path between \(x\) and \(root\), as in (32-33).

\(D(t_i,t_j,x) = \frac{wSP(t_i,t_j,x)}{wLP(x,root)}\)


\(wSP(t_i,t_j,x) = wSP(t_i,x)+wSP(t_j,x)\)


Now the distance for two terms \(t_i,t_j\) can be defined according to (34).

\(D(t_i,t_j)=\underset{x\in DisComAnc(t_i,t_j)}{min}D(t_i,t_j,x)\)


We convert distance values by the \(\frac{Arctan(.)}{\pi/2}\) function, and the measure for two GO terms \(t_i\) and \(t_j\) can be defined as in (35).

\(S(t_i,t_j) = 1-\frac{Arcatan(D(t_i,t_j))}{\pi/2}\)


Note that \(root\) refers to one of three first levels in the rDAG. So if \(DisComAnc(t_i,t_j)=\{root\}\) then \(D(t_i,t_j)=\infty\) and \(S(t_i,t_j)=0\). Also if \(t_i = t_j\) then \(D(t_i,t_j)=0\) and \(S(t_i,t_j)=1\). Finally let \(S=[s_{ij}]_{n\times m}\) be a similarity matrix for two given fenes or gene products \(g1, g2\) with GO terms \(t_{11},t_{12},\dots,t_{1n}\) and \(t_{21},t_{12},\dots,t_{2m}\) where \(s_{ij}\) is the similarity between the GO terms \(t_{1i}\) and \(t_{2j}\). We used a rcmax method to calculate similarity between \(g1, g2\), as defined in (36).



We also tested other methods on the similarity matrix, in particular average and BMA, but in general \(rcmax\) gave the best performance for TopoICSim (data not shown).

Besides all this, there’s also an algorithm for the geneset level, you can calculate interset/intraset similarities of these with (13) and (14). Or simply use R’s mean on the resulting matrix.



\(InterSetSim(S_k)=\frac{\sum_{i=1}^n\sum_{j=1}^mSim(g_{ki},g_{kj})}{n\times m}\)


(13) is equal to (14) in the specific case of comparing the geneset to itself.

All forumulas/explanations are quoted from the paper (references edited) [6].


The following example uses the pfam clan gene set to measure intraset similarity. For the single gene, EntrezID 218 and 501 are compared.

GO:0004029 GO:0004030 GO:0005515 GO:0008106 GO:0018479 GO:0008802 GO:0043878
GO:0004029 1.000 0.892 NA 0.112 0.800 0.800 0.894
GO:0004030 0.892 1.000 NA NA NA 0.804 0.896
GO:0005515 NA NA 1 NA NA NA NA
GO:0008106 0.112 NA NA 1.000 NA 0.092 0.113
GO:0018479 0.800 NA NA NA 1.000 0.937 0.806
GO:0008802 0.800 0.804 NA 0.092 0.937 1.000 NA
GO:0043878 0.894 0.896 NA 0.113 0.806 NA 1.000
GO:0016620 GO:0004029 GO:0004030 GO:0005515 GO:0008106 GO:0018479
GO:0016620 1.000 0.662 0.667 NA 0.143 0.530
GO:0004029 0.662 1.000 0.892 NA 0.112 0.800
GO:0004030 0.667 0.892 1.000 NA 0.113 0.804
GO:0005515 NA NA NA 1 NA NA
GO:0008106 0.143 0.112 0.113 NA 1.000 0.092
GO:0018479 0.530 0.800 0.804 NA 0.092 1.000
126133 221 218
126133 1.000 0.817 0.817
221 0.817 1.000 1.000
218 0.817 1.000 1.000

Here we can see the output of TopoICsim, it is a list containg 2 items;

  • AllGoPairs

The \(n\times m\) matrix of GO terms, including their similarities. Some values might be NA because these were non-occuring pairs. You can add AllGoPairs to the parameters next time you run TopoICSim, to possibly speed up computations (they will be used as pre-calculated scores to fill in occurring pairs).

  • GeneSim

The gene/geneset similarities depending on your input. Can be 1 number, or a matrix displaying all possible combinations. \(\to\) The mean of the geneset matrix shows the intraset/interset similarity.

Custom genes

Besides this, you can also define custom genes for TopoICSim. This consists of an arbitrary amount of GO terms. The custom genes have to individually defined in a named list.

custom <- list(cus1=c("GO:0016787", "GO:0042802", "GO:0005524"))
result <- GAPGOM::topo_ic_sim_genes("human", "MF", "218", "501",
  custom_genes1 = custom, drop = NULL, verbose = TRUE, progress_bar = FALSE)
#> Preparing topoICSim data...
#> Preparing term data.
#> preparing gene to GO mapping data...
#> preparing IC data...
#> Preparing gene/geneset data...
#> Started calculating all go's.
#> Filtering out precalculated values...
#> Resolving common ancestors...
#> Done!
#> Resolving disjunctive common ancestors...
#> Done!
#> Calculating short paths...
#> Done!
#> Calculating long paths...
#> Done!
#> Merging into all_go_pairs...
#> Done!
#> Merging gene(set) results...
#> Done!
#> Calculation time (in seconds):
#> 9.2574999332428
#> $GeneSim
#>        501
#> cus1 0.296
#> 218  0.979
#> $AllGoPairs
#>            GO:0004029 GO:0004030 GO:0005515 GO:0008106 GO:0018479
#> GO:0004029      1.000      0.892         NA      0.112      0.800
#> GO:0004030      0.892      1.000         NA         NA         NA
#> GO:0005515         NA         NA      1.000         NA         NA
#> GO:0008106      0.112         NA         NA      1.000         NA
#> GO:0018479      0.800         NA         NA         NA      1.000
#> GO:0008802      0.800      0.804         NA      0.092      0.937
#> GO:0043878      0.894      0.896         NA      0.113      0.806
#> GO:0016787      0.044      0.044         NA      0.032      0.035
#> GO:0042802         NA         NA      0.105         NA         NA
#> GO:0005524         NA         NA      0.125         NA         NA
#>            GO:0008802 GO:0043878 GO:0016787 GO:0042802 GO:0005524
#> GO:0004029      0.800      0.894      0.044         NA         NA
#> GO:0004030      0.804      0.896      0.044         NA         NA
#> GO:0005515         NA         NA         NA      0.105      0.125
#> GO:0008106      0.092      0.113      0.032         NA         NA
#> GO:0018479      0.937      0.806      0.035         NA         NA
#> GO:0008802      1.000      0.806      0.035         NA         NA
#> GO:0043878      0.806      1.000      0.044         NA         NA
#> GO:0016787      0.035      0.044      1.000         NA         NA
#> GO:0042802         NA         NA         NA      1.000         NA
#> GO:0005524         NA         NA         NA         NA      1.000

Here we define a custom gene named “cus1” with the GO terms; “GO:0016787”, “GO:0042802”, “GO:0005524”, it will be added to the first gene vector (218). If you want to only have a custom gene, you can define an empty vector with c() for the respective vector.

Other notes.

With TopoICSim there is a pre-calculated score matrix available, this can be turned on/off. The scores might become deprecated however, as soon as one of the org.DB packages gets an update. For this reason, we advise mostly to keep this option off. You can also pre-calculate some GO’s yourself using the custom genes. all_go_pairs can then be used as a precalculated score matrix, only intersecting/present GO terms will be used.

Parrallel processing and big data

As of right now, parallel processing is not supported. The other algorithms aren’t parallelized yet, because of implementation difficulties regarding the amount and type of dependencies that the algorithms rely on. Possibly, in the future, parallel processing will be supported. It might however be possible to divide the work in TopoICSim (on gene pair level). It runs for each unique pair of genes, which you may be able to generate using the hidden GAPGOM:::.unique_combos function. The All_go_pairs object in the result can then be combined together with other results. No support is offered however in regards to trying to make this work. Tip: The all_go_pairs argument in topo_ic_sim_genes() doesn’t automatically create a new, bigger matrix, it only uses the overlapping or present GO terms of the analysis based on the input genes.

Performance and benchmarks

The performance of this package is well tested in the Benchmarks vignette, in which benchmarks are also prepared.

Contact and support

For questions, contact, or support use the (Bioconductor) git repository or contact us via the Bioconductor forums.


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#> other attached packages:
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